Chainfeeds Summary:
IOSG Ventures' research report systematically breaks down the principles of AI training paradigms and reinforcement learning technologies, demonstrates the structural advantages of reinforcement learning × Web3, and analyzes projects such as Prime Intellect, Gensyn, Nous Research, Gradient, Grail, and Fraction AI.
Article source:
https://mp.weixin.qq.com/s/NKfN1uzojrOUy-9KtSTFPA
Article Author:
IOSG Ventures
Opinion:
IOSG Ventures: The high compatibility between Reinforcement Learning (RL) and Web3 stems from the fact that both are essentially "incentive-driven systems." RL relies on reward signals to optimize strategies, while blockchain relies on economic incentives to coordinate participant behavior, making them naturally consistent at the mechanism level. The core requirements of RL—large-scale heterogeneous rollout, reward distribution, and authenticity verification—are precisely the structural advantages of Web3. The training process of reinforcement learning can be clearly divided into two stages: 1) Rollout (exploratory sampling): The model generates a large amount of data based on the current policy, a computationally intensive but communication-sparse task. It does not require frequent communication between nodes and is suitable for parallel generation on globally distributed consumer-grade GPUs. 2) Update (parameter update): The model weights are updated based on the collected data, requiring high-bandwidth centralized nodes to complete. The "inference-training decoupling" naturally fits the decentralized heterogeneous computing power structure: Rollout can be outsourced to open networks, with settlement based on contribution through a token mechanism, while model updates remain centralized to ensure stability. Based on the deconstructive analysis of the aforementioned cutting-edge projects, we observed that although the entry points of each team (algorithm, engineering, or market) differ, when reinforcement learning (RL) is combined with Web3, their underlying architectural logic converges into a highly consistent "decoupling-verification-incentive" paradigm. This is not merely a technical coincidence, but an inevitable result of decentralized networks adapting to the unique properties of reinforcement learning. Decoupling of Rollouts & Learning—the default computational topology outsources sparse, parallelizable Rollout communication to global consumer-grade GPUs, with high-bandwidth parameter updates concentrated on a small number of training nodes, as seen in Prime Intellect's asynchronous Actor-Learner and Gradient Echo's dual-group architecture. Under this paradigm of combining reinforcement learning and Web3, the system-level advantages are primarily reflected in the rewriting of cost and governance structures. 1) Cost Restructuring: Post-training in RL has an unlimited demand for rollout sampling. Web3 can mobilize global long-tail computing power at extremely low cost, a cost advantage that centralized cloud providers cannot match. 2) Sovereign Alignment: Breaking the monopoly of large companies on AI values (alignment), the community can vote with tokens to determine "what is a good answer" for the model, thus democratizing AI governance. However, this system also faces two major structural constraints: 1) Bandwidth Wall: Despite innovations like DisTrO, physical latency still limits the full training of ultra-large parameter models (70B+). Currently, Web3 AI is more limited to fine-tuning and inference. 2) Goodhard's Law: In highly incentivized networks, miners are prone to overfitting reward rules (score farming) rather than improving real intelligence. Designing a robust reward function to prevent cheating is a perpetual game. Malicious Byzantine worker attacks: These attacks disrupt model convergence by actively manipulating and poisoning the training signal. The core strategy is not about continuously designing anti-cheating reward functions, but rather about building adversarially robust mechanisms.
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